Contamination control, including particulate monitoring, plays a role in the manufacturing processes of several industries. These industries require clean rooms or clean zones with active air filtration and require the supply of clean raw materials such as process gases, de-ionized water, chemicals, and substrates. In the pharmaceutical industry, the Food and Drug Administration requires particulate monitoring because of the correlation between detected particles in an aseptic environment and viable particles that contaminate the product produced.
Recent attention has been given to the monitoring and detection of biological agents. If aerosolized agents (biological particles) are introduced into an environment and are within the respirable range of particle sizes, then the biological particles may deposit in human lungs resulting in illness or death.
Biological contamination can occur not only in open air, but also in confined spaces, such as postal handling equipment, aircraft, hospitals, water supplies, and air ducts. Minimizing the introduction of biological particles in an environment requires the fast detection of possible pathogens. Laser-induced fluorescence of fluorescent biological substances (biofluorophores) provides a real-time technique for initially identifying samples that are candidates for containing airborne pathogens such as aerosolized bacterial spores and viruses. LIF thus may be used as a first, or detect-to-warn, stage in a biohazard detection system, with slower, but more specific tests, such as nucleic acid sequencing and/or culturing, used as additional steps or stages.
Biofluorophores significant to LIF include, tryptophan, NADH, and riboflavin or other flavinoids. But many more or less innocuous natural and man-made airborne particles also fluoresce. Consequently, some LIF systems (or “sensors”) may find it hard to distinguish harmless particles from particles of interest, like pathogens. LIF sensors are thus vulnerable to false positives—the sensor incorrectly signals the presence of threat particles. False positives can be reduced by reducing the sensitivity of the sensor to possible threat particles, but this reduces the true positive rate.
Since LIF detection of biological particles was first proposed, resources have been invested in efforts to improve hardware—like the lasers, optics, and detectors. But less effort has gone into signal processing and software. Some LIF sensors use a “region of threat” (“RoT”) approach. As each particle is detected, the sensor characterizes the particle based on fluorescence and decides whether it falls within the region of threat. If the particle falls within that region, the system counts it as a threat particle. The RoT sensor accumulates the count for a sample interval. The system senses a signal—like an alarm signal—indicating whether a threshold—like an alarm threshold—has been exceeded by the sample, and therefore whether the sample is a sample of interest. A RoT system tests the threshold against the accumulated count.
The Region of Threat approach may use a particle probability-distribution data to define the region of threat. Each of FIG. 9A and FIG. 9B is a graph of data for a large number of particles—particle spectral probability distribution data. Each particle is characterized by two-dimensional normalized fluorescence: the G/E axis represents green fluorescence normalized by elastic scatter, the B/E axis represent blue fluorescence normalized by elastic scatter. Each individual particle can be numerically characterized by a blue-green fluorescence vector, represented in FIG. 9A and FIG. 9B by a particle's location in fluorescent color space—the G/E—B/E plane. The vertical axis represents the normalized number of particles for each vector, sometimes called the bin occupancy. These figures show graphs of data for, respectively, (a) Bacillus globigii, which may be a particle of interest, and (b) kaolin, which may be a background particle. Each figure shows data for about 30,000 particles.
FIG. 8 shows an example Region of Threat. Any particle whose fluorescence falls within the RoT is treated as a threat particle; any particle whose fluorescence is outside that region is treated as background.
RoT sensors may be configured to generate an alarm based on a single particle within the region of threat. RoT sensors also may be configured to generate an alarm based on the number of threat particles detected during a sample interval. During the sample interval, the sensor counts the number of particles within the RoT. At the end of the sample interval, if the presumed threat count exceeds a preset threshold, the system sends a signal.
The RoT approach judges each particle to be a threat or not. Because some background particles may have the same fluorescence as some actual threat particles, it is not always possible to make a correct decision about each particle. Thus, the RoT approach may detect some actual background particles as presumed threat particles, and some actual threat particles as background particles. Moreover, treating each presumed threat particle identically is unlikely to be an optimal way of differentiating samples, and thus is unlikely to have an optimal probability of false positive differentiation.
Furthermore, the RoT is hard to extend to multiple dimensions. A rectangular RoT approach may be simple to implement, but may not optimally fit the actual spectral distribution of threat particles. Some regions of small threat probability may be included, while other regions of larger probability may be left out. Moreover, innocuous background materials with nearby spectral distributions may not be optimally rejected, particularly if their centroids lie obliquely to that of the threat, relative to the coordinate axes. An alternative is to define a complexly shaped RoT, but this may be computationally awkward.
Moreover, a different RoT usually must be defined for each pair of threat and background materials. Rejection of the largely unknown background is not well controlled, and no native mechanism is provided for adapting to a changing background. Instead, this is often provided ad hoc using an adaptive alarm level, which allows prescribing neither the level of detection of the threat, or the false positive rate of the background.
RoT sensors may use a time measurement to decide whether a complete sample has been acquired. Time-based sampling may be vulnerable to fluctuations in background particle concentration. (These concentrations have been observed to vary over two orders of magnitude for some applications.) For instance, consider a device that mistakes 1% of background particles for threat particles, and that alarms if 100 or more threat particles per sample are detected. If the background particle concentration is such that 1000 particles are acquired during the sample time interval, then only 10 background particles per sample are counted as threats, and the sensor will correctly not alarm. But if the background concentration increases by 100× (two orders of magnitude), then about 1000 background particles will be mistaken for threats, and the device will falsely alarm (a false positive).